The document discusses image denoising, a technique established in 1990 aimed at removing noise from images by utilizing their visual content. It outlines a new method for contrast enhancement through unsharp masking and highlights various approaches and algorithms for effective noise reduction in image processing. The findings include adaptive filters, histogram equalization, and innovative techniques for denoising, including those applicable to medical imagery.

1. image denoising projects

2. ARTICLE
• The DENOISING is the technique that is proposed in 1990.The goal of image denoising is to remove
noise by differentiating it from the signal. DENOISING uses thevisual content of images like color,
texture, and shape as the image index to retrieve the images from the database. These feature
never changed. In this project, we presents a new method for un sharp masking for contrast
enhancement of images. Image denoising is a well studied problem in the field of image processing.
Use of basic filter to remove the noise and comparative analysis b/w them. The approach employs
an adaptive median hat controls the contribution of the sharpening path in such a way that contrast
enhancement occurs in high detail areas and noise detection technique for remove mixed noise
from images. A hybrid cumulative histogram equalization is proposed for adaptive contrast
enhancement 1 . Pulmonary fissure detection in ct images using a Derivative of stick filter 2 . A fast
method of fog and haze removal 3 . A largest matching area approach to image denoising 4 . An
enhanced denoising technique using dual tree complex wavelet transform 5 . Analysis of adaptive
filter and ICA for noise Cancellation from a video frame 6 . Learning joint demosaicing and
denoising based on Sequential energy minimization 7 . sequence-to-sequence similarity-based filter
for image denoising 8 . Tensor decomposition and PCA jointed algorithm For hyperspectral image
denoising 9 . Image segmentation using parametric contours With free endpoints 10 . Difference
image and fuzzy c-means for detection Of retinal vessels